This invention relates to a computer implemented automated remote support in a computing system utilizing machine learning to provide a support capability that enhances automatically with use.
Product support is a key activity in any business and certainly, the prime focus in a business related to providing services to customers. Traditionally this support is provided in a structured manner depending on the level of support necessary. For instance, many organisations provide a three level support:
1. Level-1: Telephonic support.
2. Level-2: Country/Region expert. May include visits by an engineer
3. Level-3: Highest level of support. Provided by the maintenance/development team.
With the proliferation of products that need to be supported, as well as the rapidly increasing competitiveness as more service providers enter the market and vye for market share, it is becoming increasingly necessary to provide a constantly improving level of support. This situation is further complicated by the scarcity of trained manpower as well as the constant need for upgradation of their skills through training programs. There is now a clear realisation of an urgent need for improving the effectiveness of available manpower.
One approach to solving this problem is to provide some degree of automation in the support activity. Some attempts have been made in this direction. U.S. Pat. Nos. 5,928,369 and 5,488,732 are examples of techniques evolved for this purpose. However, none of the available methods addresses the problem adequately and in a generic manner. U.S. Pat. No. 5,928,369, for example, limits itself to a problem relating only to computer software in which the problem data is submitted in the form of a stack trace. Similarly, U.S. Pat. No. 5,488,732 deals only with a system for generating an automatic support tool and not with the support system itself.
The object of this invention is to provide a system that offers a continuously self-improving capability for automated response with respect to providing customer support.
To achieve the said objective this invention provides in a computing system including a means for electronically receiving requests, a computer implemented automated remote support comprising the combination of said computing system with:
a means for prioritising electronically received support request,
a means for parsing each said support request to extract actual and expected behaviour information and test case data, if any, and compiling actual and expected behaviour data in a prescribed format,
the said formatted output is connected to a searching means for previous occurrences of similar behaviour in a data base,
a means for generating the final solution or suggestions, using the results of the search,
a means for updating the database to refine the organization of the data in order to improve the effectiveness of subsequent search operations, the updations being performed even if no input requests are received,
a means for forwarding of the request to a system expert in case a solution is not found.
The support request is received through a mailing system or via a network.
The means for parsing the support request is a standard or custom-designed parsing system.
The searching means is a search engine.
The said database contains information relating to the functionality, specifications and behaviour, including reported problems and their solutions, for individual products as well as combinations of products.
The said database is structured as a single entity or as a combination of multiple entities in order to improve the effectiveness of searching.
The said database consists of a knowledge cache containing the most recent information relating to individual products, a multi-product view cache containing the most recent views on various product combinations, and a set of knowledge bases that contain all available information on individual products as well as product combinations,
The means for generating the final solution or suggestions is an expert system
The means for updating the database is the said expert system or different expert system with self-learning abilities.
The output of the said parsing means is further connected to an automatic test execution means for using test data extracted from the input request in order to confirm the reported behaviour as well as to obtain any additional inputs if possible, the output of said automatic test execution means being connected to the parsing means which generates the final definition of the expected and actual behaviour in a prescribed format.
The said computing system is a single computing system or a plurality of interconnected computing systems.
The test execution system is a computing system or a plurality of interconnected computing systems incorporating data storage devices, for the automatic login and execution of test cases in the right environment.
The expert system is a computing system or a plurality of interconnected computing systems incorporating data storage devices with self-learning capability utilizing machine-learning techniques such as artificial intelligence.
The knowledge cache means, is a computing system or a plurality of interconnected computing systems incorporating data storage devices, containing the most recent data on individual products.
The multiproduct view cache means is a computing system or a plurality of interconnected computing systems incorporating data storage devices, containing the most recent views of various combinations of products.
The knowledge base means is a computing system or a plurality of interconnected computing systems incorporating data storage devices, containing all available data on supported products, not necessarily the most recent data.
The data in the knowledge bases, knowledge cache and the multiproduct view cache is updated whenever the expert system identifies the occurrence of new behaviour, or when a new product or a revision to an existing product are released into the system or when a solution is provided by the system expert to a problem that was forwarded by the automatic remote support system.
The said prescribed format includes but is not limited to Environment, Description, Action, Next, Testcase to facilitate effective interfacing.
The single computing system or a plurality of interconnected computing systems is a part of a network.
In a specific embodiment an automatic remote Support comprises:
a means for prioritising electronically received support request,
a means for parsing each said support request to extract information relating to actual behaviour, expected behaviour, as well as test case data, if any, and compiling final, actual and expected behaviour data in a prescribed format,
the said formatted output is connected to a search engine to search the extracted behaviour data,
one interface of the search engine is connected to a knowledge cache means for searching for a match with previously stored data relating to individual products,
a second interface of the search engine is connected to a multiproduct view cache for searching for a match with previously stored data relating to views on combinations of products,
a third interface of the search engine is connected to knowledge bases for searching for a match with previously stored data relating to supported products, the arrangement being such that the search engine will search first through the knowledge cache and if no complete or partial match is found will then search through the multiproduct view cache followed by the knowledge bases for providing the final output,
the final interface of the search engine is connected to an expert system for providing the final solution or suggestions and follow-up questions in case a complete or partial match is found, and the request is forwarded to a system expert in case no match is found,
one interface of the said expert system provides the solution in response to the input request, while the remaining outputs update the knowledge cache, multiproduct view cache and the knowledge bases using the self-learning abilities of the expert system to refine the organization of the data in order to improve the effectiveness of subsequent search operations, the updations being performed even if no input requests are received, in a controlled manner.
The other features of the specific embodiment are the same as described above.
This invention further provides in a computing system including a means for electronically receiving requests, a method for automatically processing support requests comprising the steps of:
electronically receiving and prioritising the support requests describing expected and observed behaviour and optionally test case data,
parsing the said electronically received request to extract actual and expected behaviour information and test case data, if any,
compiling of final actual and expected behaviour data in a prescribed format,
searching for previous occurrences of similar behaviour in a database,
generating the final solution or suggestions, using the results of the search,
updating of the database in case of new behaviour data as well as by using the self-learning abilities of the expert system to refine the organization of the data in order to improve the effectiveness of subsequent search operations, the updations being performed even if no input requests are received,
forwarding of the request to a system expert in case a solution is not found.
The automatic execution of the test case data derived from the result of the said parsing, is used to confirm the reported actual behaviour.
In cases where multiple requests are received, each request is processed based on a prescribed priority handling mechanism.
In order to minimise delays, at each stage of the system a request is operated upon as soon as the previous request has been cleared without waiting for the completion of the processing of one request, providing the processing of the previous request is not compromised in any manner.
In a specific embodiment, in a computing system including a means for electronically receiving requests, a method for automatically processing support requests comprises the steps of:
electronically receiving and prioritising the support requests describing expected and observed behaviour and optionally test case data,
parsing the said electronically received request to extract actual and expected behaviour information and test case data, if any,
compiling of final actual and expected behaviour data in prescribed format,
searching for previous occurrence of similar behaviour in a knowledge cache, a multiproduct view cache followed by knowledge bases,
generating the final solution or suggestions, using the results of the search,
updating of the knowledge cache, multiproduct view cache and knowledge bases in case of new behaviour data as well as by using the self-learning abilities of the expert system to refine the organisation of the data in order to improve the effectiveness of subsequent search operations, the updations being performed even if no input requests are received,
forwarding of the request to a system expert in case a solution is not found.
The other features of this specific embodiment of the method are the same as described above.